Recent
years have witnessed the unprecedented prevalence of "Big Data".
Big Data is transforming science, engineering, medicine,
healthcare, finance, business, and ultimately, the society
itself. This year IDEAL'2013 is pleased to introduce a Special
Session on Big Data. We wish to encourage researcher to submit
high-quality original papers (including significant
work-in-progress) in any aspect of Big Data with emphasis on 5Vs
(Volume, Velocity, Variety, Value and Veracity): big data
science and foundations, big data infrastructure, big data
management, big data searching and mining, big data
privacy/security, and big data applications.

Instructions for Authors:

Authors are invited to submit their manuscripts (in pdf format) written in English by the deadline via the EasyChair online submission system.
All submissions will be refereed by experts in the field based on originality, significance, quality and clarity.

All contributions must be original, should not have been published elsewhere and must not be submitted elsewhere during the review period.
Papers should not exceed 8 pages and must comply with the format of Springer LNCS/LNAI Proceedings.

Accepted papers presented at the conference will be included in the Proceedings of IDEAL 2013, to be published by Springer in its LNCS Series, which is indexed in EI.
In addition, selected papers will be invited for special issues in several leading international journals in the field, including the International Journals of Neural Systems (IJNS).

Topics of Interests:

The topics of interests include but are not limited to:

1. Big Data Science and Foundations

Novel Theoretical Models for Big Data

New Computational Models for Big Data

Data and Information Quality for Big Data

New Data Standards

2. Big Data Infrastructure

Cloud/Grid/Stream Computing for Big Data

High Performance/Parallel Computing Platforms for Big Data

Autonomic Computing and Cyber-infrastructure, System Architectures, Design and Deployment

Energy-efficient Computing for Big Data

Programming Models and Environments for Cluster, Cloud, and Grid Computing to Support Big Data